Improved feed e ffi-ciency in particular is a goal of most fish breeding programmes, but genetic pa-rameter estimates for feed consumption and efficiency are rare [21,22,24,42].. This is bec
Trang 1DOI: 10.1051/gse:2007013
Original article
in current fishmeal and future plant-based
diet environments
Cheryl D Q a ∗, Antti K a, Juha K b,
Ossi R c
a MTT Agrifood Research Finland, Biotechnology and Food Research, Biometrical Genetics,
31600 Jokioinen, Finland
b Finnish Game and Fisheries Research Institute Jyväskylä, Survontie 9, 40500 Jyväskylä,
Finland
c Finnish Game and Fisheries Research Institute, Tervo Fisheries Research and Aquaculture,
72210 Tervo, Finland (Received 9 November 2006; accepted 20 January 2007)
Abstract – The aquaculture industry is increasingly replacing fishmeal in feeds for carnivorous
fish with soybean meal (SBM) This diet change presents a potential for genotype-environment (G × E) interactions We tested whether current salmonid breeding programmes that evaluate and select within fishmeal diets also improve growth and e fficiency on potential future SBM diets A total of 1680 European whitefish from 70 families were reared with either fishmeal- or SBM-based diets in a split-family design Individual daily gain (DG), daily feed intake (DFI) and feed e fficiency (FE) were recorded Traits displayed only weak G × E interactions as vari-ances and heritabilities did not di ffer substantially between the diets, and cross-diet genetic correlations were near unity In both diets, DFI exhibited moderate heritability and had very high genetic correlation with DG whereas FE had low heritability Predicted genetic responses demonstrated that selection to increase DG and FE on the fishmeal diet lead to favourable re-sponses on the SBM diet Selection for FE based on an index including DG and DFI achieved
at least double FE gain versus selection on DG alone Therefore, current breeding programmes
are improving the biological ability of salmonids to use novel plant-based diets, and aiding the aquaculture industry to reduce fishmeal use.
feed efficiency / genotype-environment interaction / selection / aquaculture / Coregonus
lavaretus
∗Corresponding author: cheryl.quinton@mtt.fi
Article published by EDP Sciences and available at http://www.gse-journal.org
or http://dx.doi.org/10.1051/gse:2007013
Trang 21 INTRODUCTION
The use of fishmeal in aquaculture feeds has become a major issue as the global industry continues to grow [10] Fishmeal is mostly produced from wild-caught small pelagic fish species, and is considered to be the superior protein source for teleost fishes [18] It is currently the major component of
diets for farmed carnivorous salmonids such as rainbow trout (Oncorhynchus
mykiss) and Atlantic salmon (Salmo salar) whose grower diets typically
con-tain 30–50% fishmeal (2000 statistics [35, 45]) However, several environmen-tal and economic reasons exist for reducing fishmeal use These include the decline and fluctuation of wild stocks harvested for fishmeal production due
to overfishing and natural environmental factors, instability and predicted in-creases in fishmeal prices, and negative consumer perception of the safety of fishmeal-fed products [10,34] Accordingly, feed manufacturers aim to replace fishmeal with alternatives such as plant products Soybean meal is one source
of protein that can be successfully substituted in part for fishmeal in carnivo-rous fishes’ feeds [18,23,36,41,43] Some major feed companies have pledged
to reduce fishmeal use by at least 50% by 2010, and it has been predicted that within 10 years, fish diets will be close to 100% vegetarian [39]
Aquaculture genetic improvement programmes aim to complement such production system changes Breeding programmes exist for all major farmed carnivorous fish species, and have achieved improvements in growth, feed ef-ficiency, disease resistance and product quality traits [12] Improved feed e ffi-ciency in particular is a goal of most fish breeding programmes, but genetic pa-rameter estimates for feed consumption and efficiency are rare [21,22,24,42] This is because recording methods for individual fish feed intake have only recently been implemented in large-scale genetics research [21, 22, 42]; thus
no current breeding programmes select directly for feed efficiency Further-more, all breeding programmes evaluate and select broodstock based on per-formance with current fishmeal-based diets Future plant-based diets may im-pact fish breeding programmes if genotype-environment (G× E) interactions occur G× E interactions may occur in the form of genotype re-ranking across environments, or scaling effects as indicated by environmental differences in trait genetic variation [9, 30] In a worst-case scenario, superior genotypes on
a fishmeal diet would actually be inferior on a plant-based diet This would be shown by a negative genetic correlation between diets [8] In this case, current selection on fishmeal diets would compromise performance on future plant-based diets
In this study, the impact of a novel soybean protein-based diet on selective improvement of growth, feed intake and feed efficiency was investigated in
Trang 3European whitefish (Coregonus lavaretus L.), a salmonid now farmed
com-mercially in Finland A breeding programme has recently been established to improve growth and feed efficiency in this species To assess the degree to which these traits are affected by G × E interactions when reared with either
a traditional fishmeal diet or a potential future soybean meal-based diet, we tested whether these traits express diet-specific phenotypic and genetic vari-ation, and estimated trait genetic correlations between the diets To quantify the impact of G× E interaction on selection response, we predicted genetic changes occurring on both diets in response to alternative strategies of selec-tion for growth or feed efficiency on either fishmeal or soybean meal-based diets
2 MATERIALS AND METHODS
2.1 Diet formulations
Two practical isonitrogenous and isocaloric diets were formulated (Tab I)
In the fishmeal (FM) diet, fishmeal supplied 100% of the dietary protein This diet represents a typical commercial diet used in whitefish farming In the soy-bean meal (SBM) diet, 50% of the dietary protein was replaced with SBM-derived protein This diet represents a realistic future diet Methionine, lysine and phosphorus supplements were added to the SBM diet to balance these lev-els with the FM diet Ingredients were mixed in a Hobart-type mixer, extruded (Clextral BC 45, FR) to 3.5 mm pellets, re-dried at 40–45◦C, top dressed with
fish oil and stored in a freezer until use
2.2 Population and experimental design
Whitefish in the experiment originated from the breeding programme based
at the Tervo station of the Finnish Game and Fisheries Research Institute (FGFRI) The original broodstock was established in 1998 by mating 50 wild males and 150 wild females originating from the Kokemäki River, Finland
In October 2003, the generation for the current experiment was established:
45 sires were mated with 52 dams in a partial factorial design to create 70 full-and half-sib families Each sire was mated to an average of 1.6 dams full-and each dam to an average of 1.3 sires (both ranges 1–2) Genetic relationships among sires and dams are unknown
Families were kept separate during incubation and early rearing At the eyed-egg stage (January 2004), the families were transported to the FGFRI
Trang 4Table I Formulation and analysed nutrient composition of the experimental fishmeal
and soybean meal diets.
Fishmeal (g·kg −1) Soybean meal (g·kg −1)
Ingredient
removed by sievea
Wheat starch, pregelatinized a 52.5 52.5
Analysed composition
Sources: a Raisio, FI; b Kemira, FI; c BASF, DE; d Welding GmbH, DE.
eAdded to supply (per kg diet): retinol acetate 8000 IU, cholecalciferol 3000 IU, all-race-α-tocopheryl acetate 300 IU, menadione sodium bisulfite 10 mg, thiamine HCl
21 mg, riboflavin 30 mg, calcium d-pantothenate 92 mg, biotin 0.3 mg, folic acid
6 mg, vitamin B 12 0.04 mg, niacin 120 mg, pyridoxineHCl 20 mg, ascorbic acid (35% Stay C) 900 mg, inositol 200 mg, manganese oxide (62% Mn) 100 mg, zinc oxide (74% Zn) 200 mg, potassium iodide (76% I) 6 mg.
f Calculated as 1000 – (water + protein + lipid + fibre).
Laukaa Research Station and incubated (water 4–6◦C) After hatching
(Febru-ary 2004), 100 to 150 fish per family were held in indoor 150 L fibreglass tanks (water 13–15 ◦C) and fed commercial dry diets (larvae: AgloNorse, EWOS
Ltd., NO; juveniles: Nutra Parr and Royal Silver, Raisio Ltd., FI) In June
2004, approximately 40 random individuals from each family were tagged by injecting a passive integrated transponder into the body cavity To give all the fish the same initial nutritional environment and to identify if tagging harmed
Trang 5any fish, all fish were fed with a 1:1 mixture of the two experimental diets for four weeks prior to the trial
Twenty-four tagged fish per family were randomly sampled for the diet trial
To construct a split-family design, each family was first randomly split into two groups to be reared with the alternative diets Each group was evenly dis-tributed over 6 round 0.6 m3 replicate tanks (12 experimental tanks in total) Consequently, the diet trial began with a total of 1680 fish, each tank contain-ing 140 fish (two fish from each family)
The trial was conducted from July 29 to October 21, 2004, during which fish tripled in weight Fish were fed 6 h·d−1using belt feeders Fish were counted
and bulk-weighed biweekly and feeding was adjusted according to average weight and tank biomass To ensure excess feeding, the feed amounts supplied were adjusted to be 1.3 times higher than predicted by Koskela [27] Tanks were supplied with fresh water (14.8–15.1 ◦C; flow rate 8–16 L·min−1;
out-let water O2 level> 80% saturation) and 24 h light was provided with ceiling fluorescent tubes The experimental conditions were standardized to permit ac-curate comparison of families across diets This test environment deviates from commercial circumstances where fish are reared in large outdoor net cages un-der naturally varying environmental conditions
2.3 Traits recorded
Individual body weights were recorded to the nearest g at the beginning and end of the trial (Tab II) Daily feed intake was measured by X-radiography [17]
5 times per individual, with 2-week intervals between measurements On feed intake measurement days, fish were fed with the same methods and quantity as during normal days, but feed pellets included lead glass beads (Ballotini size 8.5; Jencons Ltd., UK) visible in X-ray Afterward, fish were anaesthetized, identified, and X-rayed (Bennett HFQ 3000P X-ray machine, US)
To transform the number of glass beads fed to the amount of feed ingested, predictive regression models were established in a separate study For each diet, 16 samples of known weights were taken from the bead-labelled pel-lets and X-rayed The number of beads present in each sample was counted,
and diet-specific regression equations were obtained (R2 = 0.97−0.98) Feed intake (g) for each individual was predicted using these equations from the number of beads observed under X-ray [17]
Traits analysed were individual daily weight gain (DG), average daily feed intake (DFI) and feed efficiency (FE) Daily gain was calculated as the dif-ference between the initial and the final body weights, divided by the number
Trang 6of days in the trial (77–80 d, depending on the tank) Individual DFI was cal-culated by fitting repeated measures analysis of variance with measurement time (1–5) as the random repeated factor, and then calculating least squares means for each individual (MIXED procedure, SAS 9.1; SAS Inst Inc., US) This was done separately within each experimental tank Feed efficiency was calculated as the ratio of DG to DFI
2.4 Statistical analysis of diet e ffects
Diet effects on the means of body weights, DG, DFI and FE were tested with analysis of variance (MIXED procedure) Statistical models included diet as a fixed effect, and replicate tank nested within diet, family, and diet-family in-teraction as random effects For all traits, variance due to random experimental tank-family interaction was zero and thus was excluded Standard errors and
degrees of freedom for the F-tests of the fixed effects were calculated using the Kenward and Roger option Additional analyses were performed to stan-dardize DG and DFI to a common body weight by adding initial weight as a regression covariate to the above model
2.5 Genetic analysis
In order to examine G× E interactions, observations recorded under each diet treatment were treated as separate traits For instance, DG recorded on FM (DGFM) and SBM diets (DGSBM) were defined as different traits
Phenotypic and genetic parameters of DG, DFI and FE were estimated us-ing multiple-trait animal models with DMU software, applyus-ing restricted max-imum likelihood and average information methods [31] Models contained ex-perimental tank as a fixed effect, and full-sib family, individual genetic and residual error as random effects The individual genetic effect included additive genetic effects and parts of potential dominance effects The random full-sib family effect contained (co)variance due to common incubation and early rear-ing of full sibs, as well as parts of potential dominance (co)variances Residual covariances between traits measured in different diets were set to zero Stan-dard errors of (co)variances were obtained by a first-order Taylor series expan-sion of the average information matrix of the estimated (co)variances
Heri-tability (h2) was calculated as the ratio of genetic variance to total phenotypic variance Full-sib family effect (c2) was calculated as the ratio of full-sib fam-ily variance to total phenotypic variance When calculating trait correlations within diets, we did not estimate correlations between FE and its component
Trang 7traits because this practice can be considered statistically vague due to auto-correlation effects To aid the reader in perceiving the results, we do however present diet means, variances and heritabilities for FE
Daily gain and feed intake are commonly expressed relative to body weight Thus, the analysis for DG and DFI was also carried out using a model that included initial body weight as a covariate For this study, we use the terms
“absolute” and “relative” to refer to traits analysed without and with initial body weight as a covariate, respectively
2.6 Prediction of genetic responses to selection
Selection index calculations were used to predict responses in DG, DFI and
FE on both FM and SBM diets to phenotypic selection [15] Selection was not practiced directly for FE because when selecting on a ratio, genetic changes
in the individual component traits are very difficult to control [14] Four se-lection strategies were compared: (a) sese-lection for DGFM; (b) selection for maximum FEFM where increased DGFM and decreased DFIFM were selected for simultaneously and selection index weights were set to obtain maximum genetic change in FEFM; (c) selection for DGSBM; and (d) selection for maxi-mum FESBMwhere increased DGSBMand decreased DFISBM were selected to obtain maximum genetic change in FESBM Strategy (a) is comparable to cur-rent aquaculture breeding programmes that only select for growth rate Strate-gies (c) and (d) predict the effects of selection performed on the potential future soybean diets
Direct and correlated genetic responses to one generation of selection were
calculated by R = i (bG ) (bPb)− 1
, where R is the vector of responses, i is intensity of selection (set to 1), b is the vector of relative index weights which sum to 1, G is the genetic covariance matrix and P is the phenotypic covari-ance matrix G and P were results from the 4-trait genetic parameter estimation
model for DGFM, DFIFM, DGSBM and DFISBMdescribed above The parame-ters for FE were not needed because all results for this ratio can be predicted from its component traits DG and DFI [14]
To generate the alternative selection strategies, relative index weights were modified as follows To obtain strategy (a), the index weight for DGFM was set to 1, while the weights for the three remaining traits were zero Similarly, for strategy (c), the index weight for DGSBM was set to one For the selection strategies to maximise FE (b and d), the maximum genetic response in FE on both diets was obtained when half of the index weight was on DG and half against DFI
Trang 8Table II Least squares means (± s.e.), and statistical tests for the diet effect for traits recorded on fishmeal and soybean meal diets Denominator degrees of freedom (ddf) shown; all numerator df = 1.
Fishmeal Soybean meal Test statistics
Initial body weight (g) 818 40.9 ± 0.85 829 40.6 ± 0.87 10.0 0.78 0.3984 Final body weight (g) 765 131.2 ± 2.38 768 125.8 ± 2.24 9.7 9.36 0.0125 Daily gain (g·d −1) 765 1.145± 0.0212 768 1.091 ± 0.0201 9.5 20.0 0.0014 Daily feed intake (g ·d −1) 817 0.948± 0.0236 829 1.027 ± 0.0285 9.6 7.23 0.0235 Feed efficiency 765 1.208 ± 0.0150 768 1.078 ± 0.0240 8.5 23.2 0.0011
To calculate genetic response in FE, mean FE was first calculated for each
diet from the data, i.e., before selection Then, genetic responses to selection
were calculated for DG and DFI, and the new mean FE was calculated from these [22]
3 RESULTS
3.1 Diet di fferences
As expected, whitefish performed better on the FM diet Fish fed the FM diet had significantly higher final body weight and DG, lower DFI, and better FE than those fed the SBM diet (Tab II) These diet differences remained (results not shown) when DG and DFI were standardised by including initial body
weight as a covariate (P< 0.0001) in the statistical models
3.2 Phenotypic and genetic (co)variation
There was no evidence for diet differences in trait phenotypic or genetic variation In both diets, DG and DFI showed moderate heritability whereas
FE showed very low heritability that did not differ from zero (Tab III) For a given trait recorded on both diets, the differences in heritabilities between diets were relatively small (mean absolute difference = 0.07) and confidence inter-vals overlapped considerably Because heritabilities may remain constant even
if the underlying genetic and residual variations change, diet-specific coe ffi-cients of variation were also calculated for each trait Diet differences between coefficients of phenotypic variation for pairs of traits were small (mean abso-lute difference = 1.1%) as well Coefficients of genetic variation for absolute and relative DG, DFI and FE were only slightly higher on the FM diet (mean difference = 2.9%)
Trang 9Table III Phenotypic variance (VP ), coefficients of genetic (CV G ) and phenotypic
variation (CVP), heritability (h2± s.e.) and full-sib family effect (c2 ± s.e.) for absolute and relative (Rel.) traits measured on fish reared with fishmeal and soybean meal diets.
Diet, Trait VP CVG CVP h2 ± s.e. c2 ± s.e.
Fishmeal
Daily gain 0.134 16.2 31.9 0.26 ± 0.18 0.13 ± 0.09 Daily feed intake 0.095 15.6 32.5 0.23 ± 0.15 0.08 ± 0.07 Feed efficiency 0.044 4.3 17.4 0.06 ± 0.10 0.04 ± 0.05 Rel daily gain 0.090 17.0 26.1 0.42 ± 0.17 0.04 ± 0.07 Rel daily feed intake 0.068 15.6 27.6 0.32 ± 0.14 0.03 ± 0.06
Soybean meal
Daily gain 0.100 13.1 29.0 0.20 ± 0.15 0.08 ± 0.08 Daily feed intake 0.104 12.8 31.3 0.17 ± 0.15 0.09 ± 0.08 Feed efficiency 0.033 4.3 16.9 0.07 ± 0.11 0.07 ± 0.06 Rel daily gain 0.076 14.3 25.2 0.32 ± 0.14 0.04 ± 0.06 Rel daily feed intake 0.081 12.6 27.8 0.21 ± 0.15 0.10 ± 0.08
Genetic correlations between the same traits recorded in each diet indicated very little re-ranking of families across the diets Genetic correlations (± s.e.) between the diets for DG (0.97 ± 0.21), DFI (0.93 ± 0.28) and FE (1.00 ± 0.95) were all close to unity The large standard error of the FE correlation was likely caused by the low heritability of this trait Genetic correlations between the diets for relative DG (0.99 ± 0.13) and relative DFI (0.97 ± 0.22) were also very high
Within-diet trait correlations were similar in both diets Phenotypic (rP) and
genetic correlations (rG± s.e.) between DG and DFI were high and positive
on both FM (rP = 0.88, rG = 0.97 ± 0.05) and SBM diets (rP = 0.86, rG =
0.93 ± 0.10) Correlations between relative DG and DFI (FM rP= 0.82, rG =
0.97 ± 0.05; SBM rP = 0.82, rG = 0.96 ± 0.10) were similar to those for absolute traits
3.3 Prediction of selection responses
The selection index calculations showed that current selection on fishmeal diets will lead to strong favourable correlated genetic changes for perfor-mance on SBM diets (Fig 1) Little difference was observed between the di-ets for genetic response to selection We emphasize that although the genetic and phenotypic parameter point estimates used in the selection index differed
Trang 10Figure 1 Genetic responses in daily gain (DG), daily feed intake (DFI) and feed efficiency (FE) in fishmeal (FM) and soybean meal (SBM) diets in response to four alternative selection strategies: selection on DGFMalone, selection for maximum FEFM, selection for
DG alone, and selection for maximum FE Genetic responses are given as percent change from the original means in the data.